It has been shown in the literature that training models with the ultimate goal of maximizing accuracy leads to learning and even amplifying the biases in data.
Bias in machine learning can be defined as the phenomena of observing results which are systematically prejudiced due to faulty assumptions.
The survey by [1]} covers various types of bias, such as historic bias, representation bias and algorithmic bias.
Differential privacy is used in many contexts, including but not limited to healthcare settings [2]}, [3]}, commerce (US Census data release) [4]}, [5]} and natural language processing applications such as next word prediction in keyboards [6]}.
